Counterfactual Communities: Strategy Games, Paratexts and the Player’s Experience of History
Bibliographic record
Abstract
The genre of history strategy games is a crucial area of study because of what is at stake in the representation of controversial aspects of history in popular culture. Previous work has pointed to various affordances and constraints in the representation of history, based on the framing of the game interface, the alignment of goals with certain strategies and textual criticism of the contents of the games. In contrast, this article examines these games from the perspective of the player’s experience of play in relation to a wider gaming community. It is in these counterfactual communities that players negotiate their individual experience with their knowledge of the history that is presented in the games that they play, indicating that the relationship between digital games, players and history is highly contextual. The relevant practices of players of history strategy games are illustrated with examples from the official and unofficial communities of the Paradox Interactive games Europa Universalis II and Victoria: Empire Under the Sun. The shared paratexts demonstrate how positions are negotiated in relation to the ‘official’ version of history presented in the games. These negotiations are made tangible through the production and sharing of paratexts that remix the official history of the games to include other perspectives developed through counterfactual imaginations. These findings indicate the importance of including perspectives from gaming communities to support other forms of analysis in order to make rigorous observations about the impact of digital games on popular history.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.037 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".